Selection of the best healthcare waste disposal techniques during and post COVID-19 pandemic era

In recent years, municipal authorities especially in the developing nations are battling to select the best health care waste (HCW) disposal technique for the effective treatment of the medical wastes during and post COVID-19 era. As evaluation of various disposal alternatives of HCW and selection of the best technique requires considering various tangible and intangible criteria, this can be framed as multi-criteria decision-making (MCDM) problem. In this paper, we propose an assessment framework for the selection of the best HCW disposal technique based on socio-technical and triple bottom line perspectives. We have identified 10 criteria on which the best HCW disposal techniques to be selected based on extant literature review. Next, we use Fuzzy VIKOR method to evaluate 9 HCW disposal alternatives. The effectiveness of the proposed framework has been demonstrated with a real-life case study in Indian context. To check the robustness of the proposed methodology, we have compared the results obtained with Fuzzy TOPSIS (Technique of Order Preference Similarity to the Ideal Solution). The results help the municipal authorities to establish a methodical approach to choose the best HCW disposal techniques. Our findings indicate that incineration is the best waste disposal technique among the available alternatives. Even if the dataset indicates 'incineration' is the best method, we must not forget about the environmental concerns arising from this method. In COVID time, incineration may be the best method as indicated by the data analysis, but "COVID" should not be an excuse for causing "Environmental Pollution".

Reversal of Acute Cellular Rejection After Renal Transplantation With Campath-1H

Between September 2002 and February 2004, 40 kidney transplant (27 from deceased and 13 from living donors) recipients (25 male and 15 female, aged 50.3 +/- 15.1 years) were treated with Campath 1H (C 1H; 30 mg/dose IV) for biopsy-proven steroid-resistant rejection (SRR) or rejections equal to or worse than Banff 1B. All transplantations occurred between August 2001 and May 2003. All patients had received antibody preconditioning (RATG 5 mg/kg, n = 34; C 1H 60 mg, n = 4; C 1H 30 mg, n = 2) preoperatively and were treated with Tacrolimus monotherapy (target level 10 ng/ml) postoperatively and subsequent spaced weaning. Elevated creatinine levels at follow-up were evaluated by renal transplant biopsy. Rejection was treated with steroids, reversal of weaning, addition of sirolimus, and/or antibody treatment, depending on grade of rejection. The mean duration of follow-up was 453 +/- 163 days after C 1H administration. Twenty-nine patients received C 1H for SRR and 11 patients for Banff 1B or worse rejections; 26 patients received more than 1 dose of C 1H. Graft survival was 62.5% (25 patients); 6 of the 15 allografts (40%) that failed had presented with rejections because of noncompliance. Graft survival in compliant patients with SRR or rejections equal to or worse than Banff 1B was 73.5% (25 of 34). Fourteen patients (35%) had infectious complications, of whom 2 patients (5%) died. C 1H is an effective agent for SRR and Banff 1B or worse rejections, with 95% patient survival and 73.5% graft survival (in compliant patients). The number of doses of 30 mg C 1H should be restricted to two, as there is a high incidence of potentially fatal infectious complications.

Effect of surgical delay on survival outcomes in patients undergoing curative resection for primary hepatocellular carcinoma: Inverse probability of treatment weighting using propensity scores and propensity score adjustment

BACKGROUND

The evidence is conflicting regarding the effect of delays from the time of diagnosis to surgery on the survival in patients with hepatocellular carcinoma. We sought to investigate the impact of time to surgery on overall survival for patients who underwent curative resection for primary hepatocellular carcinoma.

METHODS

We performed a retrospective review of all patients who underwent liver resection for primary hepatocellular carcinoma between the years 2000 and 2015. Using 30-, 60-, and 90-day cutoffs, we investigated the effect of time to surgery on survival outcomes by dichotomizing the patients and using inverse probability of treatment weighting to ensure comparability. We also investigated time to surgery in prognostic subgroups by modeling the statistical interaction between time to surgery and the relevant prognostic variable in multivariable Cox models.

RESULTS

A total of 863 patients underwent liver resection for primary hepatocellular carcinoma during the study period. Using 30-, 60-, and 90-day cutoffs, time to surgery did not have a significant bearing on overall survival. For elderly patients (>70 years), patients with Child-Pugh B liver disease, American Society of Anesthesiologists status 2/3, tumor size >5cm, tumor size ≥10cm and presence of extrahepatic invasion, hazard ratio decreased and overall survival improved as time to surgery increased. However, for patients with liver cirrhosis or portal hypertension, increasing time to surgery was found to portend higher risks of death.

CONCLUSION

Time to surgery does not have a significant bearing on overall survival, and modest delays even appear to be associated with improved survival in specific subsets of patients. The importance of these findings is that patients with hepatocellular carcinoma should be fully optimized before and not rushed to surgery because of concerns of tumor progression and a diminished survival.

A multi-echelon dynamic cold chain for managing vaccine distribution

While cold chain management has been part of healthcare systems, enabling the efficient administration of vaccines in both urban and rural areas, the COVID-19 virus has created entirely new challenges for vaccine distributions. With virtually every individual worldwide being impacted, strategies are needed to devise best vaccine distribution scenarios, ensuring proper storage, transportation and cost considerations. Current models do not consider the magnitude of distribution efforts needed in our current pandemic, in particular the objective that entire populations need to be vaccinated. We expand on existing models and devise an approach that considers the needed extensive distribution capabilities and special storage requirements of vaccines, while at the same time being cognizant of costs. As such, we provide decision support on how to distribute the vaccine to an entire population based on priority. We do so by conducting predictive analysis for three different scenarios and dividing the distribution chain into three phases. As the available vaccine doses are limited in quantity at first, we apply decision tree analysis to find the best vaccination scenario, followed by a synthetic control analysis to predict the impact of the vaccination programme to forecast future vaccine production. We then formulate a mixed-integer linear programming (MILP) model for locating and allocating cold storage facilities for bulk vaccine production, followed by the proposition of a heuristic algorithm to solve the associated objective functions. The application of the proposed model is evaluated by implementing it in a real-world case study. The optimized numerical results provide valuable decision support for healthcare authorities.

Healthcare Biclustering-Based Prediction on Gene Expression Dataset

In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods.

Convalescent plasma bank facility location-allocation problem for COVID-19

With convalescent plasma being recognized as an eminent treatment option for COVID-19, this paper addresses the location-allocation problem for convalescent plasma bank facilities. This is a critical topic, since limited supply and overtly increasing cases demand a well-established supply chain. We present a novel plasma supply chain model considering stochastic parameters affecting plasma demand and the unique features of the plasma supply chain. The primary objective is to first determine the optimal location of the plasma banks and to then allocate the plasma collection facilities so as to maintain proper plasma flow within the network. In addition, recognizing the perishable nature of plasma, we integrate a deteriorating rate with the objective that as little plasma as possible is lost. We formulate a robust mixed-integer linear programming (MILP) model by considering two conflicting objective functions, namely the minimization of overall plasma transportation time and total plasma supply chain network cost, with the latter also capturing inventory costs to reduce wastage. We then propose a CPLEX-based optimization approach for solving the MILP functions. The feasibility of our results is validated by a comparison study using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and a proposed modified NSGA-III. The application of the proposed model is evaluated by implementing it in a real-world case study within the context of India. The optimized numerical results, together with their sensitivity analysis, provide valuable decision support for policymakers.

Capacity estimates for data hiding in compressed images

In this paper, we present an information-theoretic approach to obtain an estimate of the number of bits that can be hidden in still images, or, the capacity of the data-hiding channel.We show how the addition of the message signal or signature in a suitable transform domain rather than the spatial domain can significantly increase the channel capacity. Most of the state-of-the-art schemes developed thus far for data-hiding have embedded bits in some transform domain, as it has always been implicitly understood that a decomposition would help. Though most methods reported in the literature use DCT or wavelet decomposition for data embedding, the choice of the transform is not obvious.We compare the achievable data-hiding capacities for different decompositions like DCT, DFT, Hadamard, and subband transforms and show that the magnitude DFT decomposition performs best among the ones compared.

Geochemical fractionation, mobility of elements and environmental significance of surface sediments in a Tropical River, Borneo

Miri River is a tropical river in Borneo that drains on flat terrain and urbanised area and debauches into the South China Sea. This paper documents the environmental status of this river, and provides an insight into the provenance using bulk chemistry of the sediments, and brings out the geochemical mobility, bioavailability, and potential toxicity of some critical elements based on BCR sequential extraction. The sediments are intense to moderately weathered and recycled products of Neogene sedimentary rocks. The hydrodynamic characteristics of the river favoured an upstream section dominated by fine sand, while the downstream sediments are medium silt. Based on the bulk geochemistry, the Miri River sediments are moderate to considerably contaminated by Cu, Mo, and As in the upstream and by Sb, As and Cu in the downstream. The potential ecological risk values are low except Cu and a significant biological impact is expected in downstream due to Cu, As, Zn and Cr. The mobility, bioavailability and Risk Assessment Code values for Zn and Mn are higher and thus may pose moderate to very high risk to aquatic organisms. Though a high bulk concentration of Cu is observed, the association of Cu with the bioavailable fraction is low.

AI led ethical digital transformation: framework, research and managerial implications

Purpose

Digital transformation (DT) leverages digital technologies to change current processes and introduce new processes in any organisation’s business model, customer/user experience and operational processes (DT pillars). Artificial intelligence (AI) plays a significant role in achieving DT. As DT is touching each sphere of humanity, AI led DT is raising many fundamental questions. These questions raise concerns for the systems deployed, how they should behave, what risks they carry, the monitoring and evaluation control we have in hand, etc. These issues call for the need to integrate ethics in AI led DT. The purpose of this study is to develop an “AI led ethical digital transformation framework”.

Design/methodology/approach

Based on the literature survey, various existing business ethics decision-making models were synthesised. The authors mapped essential characteristics such as intensity and the individual, organisational and opportunity factors of ethics models with the proposed AI led ethical DT. The DT framework is evaluated using a thematic analysis of 23 expert interviews with relevant AI ethics personas from industry and society. The qualitative data of the interviews and opinion data has been analysed using MAXQDA software.

Findings

The authors have explored how AI can drive the ethical DT framework and have identified the core constituents of developing an AI led ethical DT framework. Backed by established ethical theories, the paper presents how DT pillars are related and sequenced to ethical factors. This research provides the potential to examine theoretically sequenced ethical factors with practical DT pillars.

Originality/value

The study establishes deduced and induced ethical value codes based on thematic analysis to develop guidelines for the pursuit of ethical DT. The authors identify four unique induced themes, namely, corporate social responsibility, perceived value, standard benchmarking and learning willingness. The comprehensive findings of this research, supported by a robust theoretical background, have substantial implications for academic research and corporate applicability. The proposed AI led ethical DT framework is unique and can be used for integrated social, technological and economic ethical research.

Risk assessment in supply chains: a state-of-the-art review of methodologies and their applications

The year 2020 can be earmarked as the year of global supply chain disruption owing to the outbreak of the coronavirus (COVID-19). It is however not only because of the pandemic that supply chain risk assessment (SCRA) has become more critical today than it has ever been. With the number of supply chain risks having increased significantly over the last decade, particularly during the last 5 years, there has been a flurry of literature on supply chain risk management (SCRM), illustrating the need for further classification so as to guide researchers to the most promising avenues and opportunities. We therefore conduct a bibliometric and network analysis of SCRA publications to identify research areas and underlying themes, leading to the identification of three major research clusters for which we provide interpretation and guidance for future work. In doing so we focus in particular on the variety of parameters, analytical approaches, and characteristics of multi-criteria decision-making techniques for assessing supply chain risks. This offers an invaluable synthesis of the SCRA literature, providing recommendations for future research opportunities. As such, this paper is a formidable starting point for operations researchers delving into this domain, which is expected to increase significantly also due to the current pandemic.

Improved DeTraC Binary Coyote Net-Based Multiple Instance Learning for Predicting Lymph Node Metastasis of Breast Cancer From Whole-Slide Pathological Images

BACKGROUND

Early detection of lymph node metastasis in breast cancer is vital for improving treatment outcomes and prognosis.

METHODS

This study introduces an Improved Decompose, Transfer, and Compose Binary Coyote Net-based Multiple Instance Learning (ImDeTraC-BCNet-MIL) method for predicting lymph node metastasis from Whole Slide Images (WSIs) using multiple instance learning. The method involves segmenting WSIs into patches using Otsu and double-dimensional clustering techniques. The developed multiple instance learning approach introduces a paradigm into computational pathology by shaping pathological data and constructing features. ImDeTraC-BCNet-MIL was utilised for feature generation during both training and testing to differentiate lymph node metastasis in WSIs.

RESULTS

The proposed model achieves the highest accuracy of 95.3% and 99.8%, precision values of 98% and 99.8%, and recall rates of 92.9% and 99.8% on the Camelyon16 and Camelyon17 datasets.

CONCLUSIONS

These findings underscore the effectiveness of ImDeTraC-BCNet-MIL in enhancing the early detection of lymph node metastasis in breast cancer.

Integrating attention networks into a hybrid model for HER2 status prediction in breast cancer

Breast cancer is one of the most prevalent cancers amongst women, caused by uncontrolled cell growth in breast tissue. Human Epidermal growth factor Receptor 2 (HER2) proteins play a vital role in regulating normal breast cell development and division, and the status is crucial for determining prognosis and treatment strategies. Despite the availability of various techniques to identify the HER2 gene in tumors, the prediction accuracy of existing methods remains insufficient. This research aims to improve HER2 status prediction accuracy by proposing an Enhanced Hybrid Model with Optimized Attention Network (EHMOA-net) for histopathology image analysis. The methodology involves patch segmentation using an Encoder-Decoder-based hybrid weights alignment with Multi-Dilated U-net (EDMDU) model applied to the TCGA dataset, followed by preprocessing through enhanced Macenko stain normalization for segmented patches and images from the BCI dataset. Improved non-subsampled shearlet transform is utilized for feature extraction, and the Hybrid Enhanced Rough k-means clustering and Fuzzy C-Means (HERFCM) algorithm is employed to cluster neighboring image patches based on similar features. Finally, HER2 prediction is performed using nested graph neural networks integrated with a visual attention network. The proposed method, implemented in Python, achieves an accuracy of 97.85 %, surpassing existing techniques. These findings demonstrate the effectiveness of EHMOA-net in improving HER2 prediction accuracy and its potential utility in clinical applications.

A mathematical framework for estimating prototyping cost considering transition of quality states under Markovian setting

Purpose

This research aims to aid product development managers to estimate the expected cost associated with the development of cost-intensive physical prototypes considering transitions associated with pertinent states of quality of the prototype and corresponding decision policies under the Markovian setting.

Design/methodology/approach

The authors evolve two types of optimization-based mathematical models under both deterministic and randomized policies. Under the deterministic policy, the product development managers take certain decisions such as “Do nothing,” “Overhaul,” or “Replace” corresponding to different quality states of prototype such as “Good as new,” “Functional with minor deterioration,” “Functional with major deterioration” and “Non-functional.” Under the randomized policy, the product development managers ascertain the probability distribution associated with these decisions corresponding to various states of quality. In both types of mathematical models, i.e. related to deterministic and randomized settings, minimization of the expected cost of the prototype remains the objective function.

Findings

Employing an illustrative case of the operator cabin from the construction equipment domain, the authors ascertain that randomized policy provides us with better decision interventions such that the expected cost of the prototype remains lower than that associated with the deterministic policy. The authors also ascertain the steady-state probabilities associated with a prototype remaining in a particular quality state. These findings have implications for product development budget, time to market, product quality, etc.

Originality/value

The authors’ work contributes toward the development of optimization-driven mathematical models that can encapsulate the nuances related to the uncertainty of transition of quality states of a prototype, decision policies at each quality state of the prototype while considering such facets for all constituent subsystems of the prototype. As opposed to a typical prescriptive study, their study captures the inherent uncertainties associated with states of quality in the context of prototype testing, etc.

Deep volcanic residual U-Net for nodal metastasis (Nmet) identification from lung cancer

Lymph node metastasis detections are more clinically significant task associated with the presence and reappearance of lung cancer. The development of the computer-assisted diagnostic approach has greatly supported the diagnosis of human disorders in the field of medicine including lung cancer. Lung cancer treatment is possible if it is detected at the initial stage. Radiologists have great difficulty identifying and categorizing lung cancers in the initial phase. So, several methods were used to predict the lung cancer but does not provide accurate solutions with increased error rate. To overcome these issues, a Deep Volcanic Residual U-Net (DVR U-Net) for nodal metastasis is proposed in this manuscript which identifies the LC accurately in the early stage. Initially, the input images are taken from two datasets. After that, these input data are pre-processed using Anisotropic Diffusion Filter with a Fuzzy based Contrast-Limited Adaptive Histogram Equalization (ADFFCLAHE) method. Then the pre-processed images are given to the DVR U-Net to segment and extract the volume of interest for estimating the nodal stage of each volume of interest. Finally, DVR U-Net effectively detects and classifies the N + (nodal metastasis) or N- (non-nodal metastasis). The introduced method attains 99.9% higher accuracy as compared with the existing methods. Also, the statistical analysis of the Shapiro-Wilk test, Friedman test and Wilcoxon Signed-Rank test are executed to prove the statistical effectiveness of the implemented method.

Leveraging Radiomics and Hybrid Quantum-Classical Convolutional Networks for Non-Invasive Detection of Microsatellite Instability in Colorectal Cancer

PURPOSE

The goal of this study is to create a novel framework for identifying MSI status in colorectal cancer using advanced radiomics and deep learning strategies, aiming to enhance clinical decision-making and improve patient outcomes in oncology.

PROCEDURES

The study utilizes histopathological slide images from the NCT-CRC-HE-100 K and PAIP 2020 databases. Key procedures include self-attentive adversarial stain normalization for data standardization, tumor delineation via a Slimmable Transformer, and radiomics feature extraction using a hybrid quantum-classical neural network.

RESULTS

The proposed system reaches 99% accuracy when identifying colorectal cancer MSI status. It shows the model is good at telling the difference between MSI and MSS tumors and can be used in real medical care for cancer.

CONCLUSIONS

Our research shows that the new system improves colorectal cancer MSI status determination better than previous methods. Our optimized processing technology works better than other methods to divide and analyze tissue features making the system good for improving patient care decisions.

The Study of Correlation of Incidence, Severity of Renal, Cardiovascular Complications with Duration, Severity of Type 2 Diabetes Mellitus in a Tertiary Care Hospital

Background: Type 2 Diabetes Mellitus (DM) is a disorder of the endocrine characterised by hyperglycaemia which results from variable degrees of insulin resistance and insulin deficiency.Chronic hyperglycaemia in diabetes may lead to multi organ damage resulting in renal, cardiovascular and other complications.In our study, we aim to look for correlation between the degree of glycemic control, duration of type 2 DM, incidence, severity of renal, cardiovascular complications in type 2 DM patients. The objective of our study is to analyse the correlation between glycemic control and occurrence of cardiovascular, renal complications in type 2 DM patients. Materials and Methods: 50 type 2 DM patients were selected from the Medicine outpatient of Saveetha Medical College and Hospital from January 2021 to March 2021.The study was explained and informed consent was obtained. Ethical committee clearance was obtained.The duration of the disease, regularity of treatment are recorded, serum HbA1c was done to evaluate the degree of glycemic control.Renal function tests like estimation of urea and creatinine are done to look for renal complications. Echocardiogram was done to evaluate the cardiac status of the patient. Expected Outcome: We expect a direct correlation between the severity of uncontrolled hyperglycaemia, duration of the disease with the incidence of renal and cardiovascular complications. Results: 50 patients who were selected for the study having type 2 Diabetes Mellitus, were made into two groups - people with uncontrolled diabetes (HbA1c >7.5%) were more prone in developing renal and cardiac complications which were assessed by urea, creatinine, urine protein levels and ejection fraction (EF %) values. The significant cut off values to cause complications were taken as for urea (>40mg/dl), creatinine (>1mg/dl), urine protein (+/++/+++), EF value(>50%) and the presence/absence of regional wall motion abnormality (RWMA) was noted. It was also observed that longer age duration of the disease, more was the risk to develop cardiac complications than disease of shorter duration. Hence a poor control of hyperglycaemia made the subject prone to renal and cardiovascular complications. Conclusion: We arrive at a direct correlation between the severity and extent of uncontrolled hyperglycaemia with the incidence of severity and complications in the form of nephropathy and cardiac dysfunction.

Identifying cancer risks using spectral subset feature selection based on multi-layer perception neural network for premature treatment

Recently, human beings have been affected mainly by dreadful cancer diseases. Predicting cancer risk levels is a major challenge in biomedical research for feature selection and classification at the margins. To resolve this problem, we propose a Subset Clustering-Based Feature Selection using a Multi-Layer Perception Neural Network (SCFS-MLPNN). Initially, pre-processing is carried out with Intensive Mutual Disease Influence Rate (IMDIR) to identify the relational features. In addition, the Successive Disease Pattern Stimulus Rate (SDPSR) is carried out to create relative feature patterns. Based on the patterns, the features are selected and grouped into clustering. Inter-Class Sub-Space Clustering (ICSSC) is applied to split the features by class labels depending on the marginal rate. From the class labels, marginal features are obtained using spectral subset feature selection (SSFS). The selected features are then trained in a Multi-Layer Perception Neural Network (MLPNN) classifier to classify the patient features by risk. Its contribution is to exploit subset features to improve classification accuracy by clustering relational features. The proposed classifier yields higher classification accuracy than previous methods and observes cancer detection for early detection. Therefore, the proposed method achieved a risk analysis accuracy of 91.8% and an F-measure of 91.3% for early detection, which is recommended for early diagnosis.

Ensemble classifier fostered detection of arrhythmia using ECG data

Electrocardiogram (ECG) is a non-invasive medical tool that divulges the rhythm and function of the human heart. This is broadly employed in heart disease detection including arrhythmia. Arrhythmia is a general term for abnormal heart rhythms that can be identified and classified into many categories. Automatic ECG analysis is provided by arrhythmia categorization in cardiac patient monitoring systems. It aids cardiologists to diagnose the ECG signal. In this work, an Ensemble classifier is proposed for accurate arrhythmia detection using ECG Signal. Input data are taken from the MIT-BIH arrhythmia dataset. Then the input data was pre-processed using Python in Jupyter Notebook which run the code in an isolated manner and was able to keep code, formula, comments, and images. Then, Residual Exemplars Local Binary Pattern is applied for extracting statistical features. The extracted features are given to ensemble classifiers, like Support vector machines (SVM), Naive Bayes (NB), and random forest (RF) for classifying the arrhythmia as normal (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion beat (F), and unknown beat (Q). The proposed AD-Ensemble SVM-NB-RF method is implemented in Python. The proposed AD-Ensemble SVM-NB-RF method is 44.57%, 52.41%, and 29.49% higher accuracy; 2.01%, 3.33%, and 3.19% higher area under the curve (AUC); and 21.52%, 23.05%, and 12.68% better F-Measure compared with existing models, like multi-model depending on the ensemble of deep learning for ECG heartbeats arrhythmia categorization (AD-Ensemble CNN-LSTM-RRHOS), ECG signal categorization utilizing VGGNet: a neural network based classification method (AD-Ensemble CNN-LSTM) and higher performance arrhythmic heartbeat categorization utilizing ensemble learning along PSD based feature extraction method (AD-Ensemble MLP-NB-RF).